


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__a	 =      8
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_=BITS )    ->Tuple:
   UpperCAmelCase      					=  x.device

   UpperCAmelCase      					=  (x * 2_5_5).int().clamp(0	,    2_5_5 )

   UpperCAmelCase      					=  2 ** torch.arange(bits - 1	,    -1	,    -1	,    device=UpperCamelCase_ )
   UpperCAmelCase      					=  rearrange(UpperCamelCase_	,    """d -> d 1 1""" )
   UpperCAmelCase      					=  rearrange(UpperCamelCase_	,    """b c h w -> b c 1 h w""" )

   UpperCAmelCase      					=  ((x & mask) != 0).float()
   UpperCAmelCase      					=  rearrange(UpperCamelCase_	,    """b c d h w -> b (c d) h w""" )
   UpperCAmelCase      					=  bits * 2 - 1
   return bits
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_=BITS )    ->Optional[Any]:
   UpperCAmelCase      					=  x.device

   UpperCAmelCase      					=  (x > 0).int()
   UpperCAmelCase      					=  2 ** torch.arange(bits - 1	,    -1	,    -1	,    device=UpperCamelCase_	,    dtype=torch.intaa )

   UpperCAmelCase      					=  rearrange(UpperCamelCase_	,    """d -> d 1 1""" )
   UpperCAmelCase      					=  rearrange(UpperCamelCase_	,    """b (c d) h w -> b c d h w"""	,    d=8 )
   UpperCAmelCase      					=  reduce(x * mask	,    """b c d h w -> b c h w"""	,    """sum""" )
   return (dec / 2_5_5).clamp(0.0	,    1.0 )
def 	_UpperCamelCase					(    self	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ = 0.0	,    lowerCAmelCase_ = True	,    lowerCAmelCase_=None	,    lowerCAmelCase_ = True	,    )    ->Union[DDIMSchedulerOutput, Tuple]:
   if self.num_inference_steps is None:
      raise ValueError(
          """Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler""" )

   # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
   # Ideally, read DDIM paper in-detail understanding

   # Notation (<variable name> -> <name in paper>
   # - pred_noise_t -> e_theta(x_t, t)
   # - pred_original_sample -> f_theta(x_t, t) or x_0
   # - std_dev_t -> sigma_t
   # - eta -> η
   # - pred_sample_direction -> "direction pointing to x_t"
   # - pred_prev_sample -> "x_t-1"

   # 1. get previous step value (=t-1)
   UpperCAmelCase      					=  timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   UpperCAmelCase      					=  self.alphas_cumprod[timestep]
   UpperCAmelCase      					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

   UpperCAmelCase      					=  1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   UpperCAmelCase      					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   UpperCAmelCase      					=  self.bit_scale
   if self.config.clip_sample:
      UpperCAmelCase      					=  torch.clamp(UpperCamelCase_	,    -scale	,    UpperCamelCase_ )

   # 5. compute variance: "sigma_t(η)" -> see formula (16)
   # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
   UpperCAmelCase      					=  self._get_variance(UpperCamelCase_	,    UpperCamelCase_ )
   UpperCAmelCase      					=  eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      UpperCAmelCase      					=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

   # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   UpperCAmelCase      					=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

   # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   UpperCAmelCase      					=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if eta > 0:
      # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
      UpperCAmelCase      					=  model_output.device if torch.is_tensor(UpperCamelCase_ ) else """cpu"""
      UpperCAmelCase      					=  torch.randn(model_output.shape	,    dtype=model_output.dtype	,    generator=UpperCamelCase_ ).to(UpperCamelCase_ )
      UpperCAmelCase      					=  self._get_variance(UpperCamelCase_	,    UpperCamelCase_ ) ** 0.5 * eta * noise

      UpperCAmelCase      					=  prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=UpperCamelCase_	,    pred_original_sample=UpperCamelCase_ )
def 	_UpperCamelCase					(    self	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_="epsilon"	,    lowerCAmelCase_=None	,    lowerCAmelCase_ = True	,    )    ->Union[DDPMSchedulerOutput, Tuple]:
   UpperCAmelCase      					=  timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
      UpperCAmelCase					,	UpperCAmelCase      					=  torch.split(UpperCamelCase_	,    sample.shape[1]	,    dim=1 )
   else:
      UpperCAmelCase      					=  None

   # 1. compute alphas, betas
   UpperCAmelCase      					=  self.alphas_cumprod[t]
   UpperCAmelCase      					=  self.alphas_cumprod[t - 1] if t > 0 else self.one
   UpperCAmelCase      					=  1 - alpha_prod_t
   UpperCAmelCase      					=  1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if prediction_type == "epsilon":
      UpperCAmelCase      					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      UpperCAmelCase      					=  model_output
   else:
      raise ValueError(F"""Unsupported prediction_type {prediction_type}.""" )

   # 3. Clip "predicted x_0"
   UpperCAmelCase      					=  self.bit_scale
   if self.config.clip_sample:
      UpperCAmelCase      					=  torch.clamp(UpperCamelCase_	,    -scale	,    UpperCamelCase_ )

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase      					=  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   UpperCAmelCase      					=  self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   UpperCAmelCase      					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   UpperCAmelCase      					=  0
   if t > 0:
      UpperCAmelCase      					=  torch.randn(
          model_output.size()	,    dtype=model_output.dtype	,    layout=model_output.layout	,    generator=UpperCamelCase_ ).to(model_output.device )
      UpperCAmelCase      					=  (self._get_variance(UpperCamelCase_	,    predicted_variance=UpperCamelCase_ ) ** 0.5) * noise

   UpperCAmelCase      					=  pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=UpperCamelCase_	,    pred_original_sample=UpperCamelCase_ )







class 	__lowercase					(	snake_case__	):





      def __init__(		self    :       List[Any]       ,							__lowerCamelCase    :       Union[str, Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Dict = 1.0       ,							)     -> List[str]:






         """simple docstring"""







         super().__init__()
         UpperCAmelCase      					=  bit_scale
         UpperCAmelCase      					=  (
             ddim_bit_scheduler_step if isinstance(_UpperCAmelCase       ,							_UpperCAmelCase							) else ddpm_bit_scheduler_step
         )

         self.register_modules(unet=_UpperCAmelCase       ,							scheduler=_UpperCAmelCase							)





      @torch.no_grad()
      def __call__(		self    :       Any       ,							__lowerCamelCase    :       List[Any] = 2_5_6       ,							__lowerCamelCase    :       Union[str, Any] = 2_5_6       ,							__lowerCamelCase    :       List[str] = 5_0       ,							__lowerCamelCase    :       Tuple = None       ,							__lowerCamelCase    :       Union[str, Any] = 1       ,							__lowerCamelCase    :       int = "pil"       ,							__lowerCamelCase    :       Any = True       ,							**__lowerCamelCase    :       Union[str, Any]       ,							)     -> Union[Tuple, ImagePipelineOutput]:






         """simple docstring"""







         UpperCAmelCase      					=  torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)       ,							generator=_UpperCAmelCase       ,							)
         UpperCAmelCase      					=  decimal_to_bits(_UpperCAmelCase							) * self.bit_scale
         UpperCAmelCase      					=  latents.to(self.device							)

         self.scheduler.set_timesteps(_UpperCAmelCase							)

         for t in self.progress_bar(self.scheduler.timesteps							):
            # predict the noise residual
            UpperCAmelCase      					=  self.unet(_UpperCAmelCase       ,							_UpperCAmelCase							).sample

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase      					=  self.scheduler.step(_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase							).prev_sample

         UpperCAmelCase      					=  bits_to_decimal(_UpperCAmelCase							)

         if output_type == "pil":
            UpperCAmelCase      					=  self.numpy_to_pil(_UpperCAmelCase							)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=_UpperCAmelCase							)








'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 							a__( snake_case__     ):
			a_	: int									=      '''efficientnet'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.5					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**_UpperCAmelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =dropout_rate
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(_UpperCAmelCase						) * 4





class 							a__( snake_case__     ):
			a_	: List[str]									=      version.parse('''1.11'''     )






			@property
			def 						_lowercase  (     self						)							->							Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					    ]						)






			@property
			def 						_lowercase  (     self						)							->							float:
					return 1E-5




